The Long View – Episode Summary

Episode Title: Scott Bondurant: Why Mean Reversion Means Your Portfolio Should Have More Stocks
Podcast: The Long View – Morningstar
Hosts: Christine Benz, Dan Lefkovitz, Amy C. Arnott
Guest: Scott Bondurant, Founder & CIO, Bondurant Investment Advisory
Date: September 9, 2025

Episode Overview

This episode explores why mean reversion—the tendency for asset returns to move towards long-term averages—should play a prominent role in portfolio construction and financial planning. Scott Bondurant, investment advisory founder and Northwestern adjunct professor, shares his career journey, the academic and practical evidence for mean reversion, its major implications for equity allocations (especially for retirees), and how both industry practice and behavioral biases lag behind these insights.

Key Discussion Points & Insights

Scott Bondurant’s Background (01:57–08:00)

• Unorthodox Career Path: Scott transitioned from being a top-ranked collegiate and professional tennis player to investment banking, prompted by dissatisfaction with law and influenced by friends in finance.
• Wall Street Evolution: He describes the 1990s as a “boom time” for sell-side research; major disruptions (internet, Reg FD, and industry scandals) eventually led him to hedge funds, teaching, and launching his own advisory.

“We had a monopoly on information flow...that was a lot of fun. And I think I would have kept doing it if it weren't for...the internet, Reg FD, and then Elliott Spitzer.”
—Scott Bondurant (04:34)

Impact of Regulatory Changes (08:00–08:54)

• Reg FD: Scott and the hosts agree with Charlie Ellis that Reg FD was “seismic” for active management, as public information levels the playing field and increases market efficiency.

“The whole thing got to be just much more efficient. That was a huge moment for active management.”
—Scott Bondurant (08:22)

Lessons from Teaching & Market History (09:04–14:13)

• Value of Academic Lens: Teaching undergraduates and revisiting classic investing books keeps Scott’s perspectives fresh and grounded.
• Cyclicality and Bubbles: Market history is characterized by recurring bubbles (“devil take the hindmost”) and the psychological belief that “it's different this time,” most recently echoed in AI speculation.
• Compounding's Power: Scott reminds listeners not to underestimate compounding and the value of dollar-cost averaging, citing Einstein.

“Never underestimate the power of compounding...Einstein said compound interest is the greatest mathematical discovery of all time.”
—Scott Bondurant (14:13)

Investing in Bubble-like Environments (14:39–15:40)

• Tactical Allocation: When broad markets are overvalued, Scott’s models point to reducing exposure to those segments in favor of value stocks, globally and in US small-cap equities—areas he sees as still attractively priced.

Why Mean Reversion Matters (15:40–19:40)

• Mean Reversion vs. Random Walk: Scott first became convinced of mean reversion after reading Siegel’s Stocks for the Long Run, finding evidence that long-term stock investors face less risk than perceived.
• Analytical Method: With academic partners and using block bootstrap Monte Carlo simulations, he compared traditional (“random walk”) and mean-reverting models, finding profound differences in withdrawal safety and drawdown risks.

“It was very clear...stocks do mean revert over time and very powerful.”
—Scott Bondurant (17:44)

Why Do Stocks Mean Revert? (19:40–21:54)

• Fundamental Reasons: High profits and valuations invite competition, reducing excess returns; unprofitable sectors see players exit, restoring profitability.
• Behavioral Reasons: Crowd psychology leads to over- and underpricing, eventually corrected.
• Empirical Verdict: “Mean reversion is in the numbers.”

How Long for Mean Reversion? (21:54–22:36)

• Timeframe: Typically about 7 years on average, but highly variable—“That’s the million-dollar question.”

Using CAPE and Navigating "False Positives" (22:36–26:17)

• CAPE Ratio: Scott prefers the CAPE due to its long-term focus and its basis in reported, not pro-forma, earnings.
• Elevated CAPEs: While CAPEs can remain high (as in the ’90s), history shows eventual mean reversion. He warns against assuming “it’s different this time.”

“History shows...‘it’s different this time’ really doesn’t play out.”
—Scott Bondurant (25:40)

Bonds: Mean Avert, Not Mean Revert (26:35–28:29)

• Bond Volatility: Unlike stocks, bond returns tend to “mean avert”—unexpected returns are followed by similarly directed future returns, especially during inflation. Bonds may therefore be riskier than standard models imply.

“Bonds are more risky than you think... When you lose that much money in real terms, it’s very hard kind of to make it back up.”
—Scott Bondurant (28:00)

Retirement Planning: Flaws of the Random Walk Model (29:21–33:16)

• Random Walk Limitation: Traditional models miss that after big gains/losses, future returns are symmetric (like a coin flip), ignoring mean reversion’s correction. This exaggerates risk and results in overly conservative withdrawal rates.
• Practical Up Shot: Incorporating mean reversion typically supports higher equity allocations and less conservative withdrawal rates.

“If you don’t incorporate mean reversion [and] just use random walk, you’re going to get flawed results.”
—Scott Bondurant (33:14)

Sequence of Returns Risk and Buffering (33:16–35:38)

• Three-Year Cash Buffer: To mitigate risk of forced selling, Scott has clients hold three years of cash or guaranteed income, noting that most bear markets recover within this window.

“If you have three years [in cash], you’re in a pretty good position...just making sure clients don’t sell during bear markets.”
—Scott Bondurant (34:06)

Practical Withdrawal Rates (35:38–37:17)

• A Rule of Thumb: For an 85/15 equity portfolio, a 5% withdrawal rate resulted in only a 7% risk of running out over a 30-year period—counter to industry norms.

“An 85/15 equity portfolio and a 5% withdrawal rate leads to a...7% chance of going bust, which is pretty reasonable.”
—Scott Bondurant (36:22)

Why the Industry Resists Mean Reversion (37:17–39:57)

• Institutional Inertia: Institutions are focused on short-term horizons; mean-variance optimization is entrenched and simple; changing dogma is hard for established firms.
• RIAs Leading Change: Independent advisors are more willing to adopt new frameworks.

Industry Bias Toward Underspending (39:57–41:26)

• Behavioral & Business Incentives: Firms prefer underspending to avoid client blowups and preserve AUM, reinforcing 60/40 allocations and low equity risk.

“There’s an incentive...to keep people spending less, and...to promote the 60/40 and lower equity portfolio because there’s less drawdown risk.”
—Scott Bondurant (40:21)

Practical Application: GMO's NEBO Platform (41:26–43:34)

• How It Works: NEBO incorporates custom capital market assumptions and allows for segment-based excess return estimates, reverting to long-term means over a set period (typically seven years).

Risk Tolerance Assessment (43:34–47:24)

• Beyond Standard Questionnaires: Scott emphasizes deeper conversations, behavioral history, and “buy-in” over reliance on generic risk profiles. He notes how counterintuitive it is that portfolios over-weighted to bonds may actually carry higher long-term risk of running out of money.

“It's really important that you have a robust questionnaire...Frankly, that’s...a suboptimal way to give advice to people.”
—Scott Bondurant (47:04)

Platform Tennis – A Personal Note (48:01–50:12)

• Platform vs. Pickleball: A winter sport, played on a small outdoor heated court with walls; popular in northern cities and notable for its social camaraderie.

Retirement Philosophy & Sustaining Purpose (50:12–51:51)

• On Retirement Happiness: Scott references Brian Portnoy’s The Geometry of Wealth, distinguishing between being “rich” and “wealthy” (the latter defined as autonomy, purpose, and community).
• Why Keep Working? Scott enjoys teaching, advising, tennis, and insists he has found the sustainable “long view.”

“You, you basically want to do stuff that you really enjoy and have a lot of autonomy and...helping society more broadly. I kind of think I've been able to do this.”
—Scott Bondurant (50:37)

Notable Quotes & Memorable Moments

• On Market History: “It really is very much the history of speculations and booms and busts, and they kind of are consistent over the course of time, and I imagine they're going to continue to be.” (10:33)
• On AI Bubble Signs: “All of the valuation historical value metrics are extremely expensive and obviously driven by the biggest names in AI...There's tons of media hype and public enthusiasm.” (13:12)
• On Mean Reversion’s Timeframe: “We use about a seven-year mean reversion timeframe. That's, I think, a reasonably good average.” (22:18)
• On Bonds’ Hidden Risks: “Bonds show increased volatility relative to the standard random walk methodologies would indicate...It's very hard kind of to make it back up.” (27:05)
• On Comfort With Drawdowns: “You are likely to have...bear markets are going to happen, you're going to have significant drawdowns. And so, when they do happen, you kind of say, this is kind of what we talked about.” (44:33)

Timestamps for Important Segments

• Bondurant’s Career Journey: 01:57–08:00
• Reg FD’s Impact: 08:00–08:54
• Lessons from Teaching & Market History: 09:04–14:13
• Responding to Current Market Bubble: 14:13–15:40
• Why Mean Reversion, Not Random Walk: 15:40–19:40
• Fundamental & Behavioral Drivers of Mean Reversion: 19:40–21:54
• How Long to Mean Revert? 21:54–22:36
• CAPE Ratio, Valuations & False Positives: 22:36–26:17
• Bond Risks & “Mean Aversion”: 26:35–28:29
• Retirement Math & Model Flaws: 29:21–33:16
• Mitigating Sequence of Returns Risk: 33:16–35:38
• Withdrawal Rates & Evidence: 35:38–37:17
• Industry Inertia & Bias: 37:17–41:26
• Practical Tools & NEBO Discussion: 41:26–43:34
• Risk Tolerance in Practice: 43:34–47:24
• Platform Tennis Primer: 48:01–50:12
• Retirement Mindset: 50:12–51:51

Takeaways

• Mean Reversion is Central: Incorporating mean reversion into your investment and retirement models can justify higher long-term equity allocations and less conservative withdrawal rates.
• Current Industry Norms Lag Behind: Most retirement planning software still assumes random walk returns and may encourage unnecessary “underspending” and suboptimal asset allocation.
• Approach Risk Honestly: Use a thoughtful, customized approach for assessing client risk tolerance and prepare for sequence risk with a cash buffer.
• Valuations Matter, but Don’t Get Trapped: High-level indicators like CAPE provide context for global and segment-level opportunities, even when “this time feels different.”
• Pursue Wealth, Not Just Riches: True financial well-being is tied to purpose, community, and autonomy, not just portfolio balances.

For listeners and planners, this episode advocates for challenging old assumptions and using robust, empirically grounded models to improve long-term investment success.